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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
An integrative approach to inferring gene regulatory module networks
Michael Baitaluk1, Sergey Kozhenkov, Julia Ponomarenko
1San Diego Supercomputer Center, University of California-San Diego, La Jolla, CA, USA.
Plos One
|January 4, 2013
Summary
This study introduces an integrated bioinformatics application for analyzing gene regulatory networks (GRNs). It simplifies the inference, analysis, and visualization of GRNs and regulatory modules, saving time and resources.
Area of Science:
- Bioinformatics
- Systems Biology
- Computational Biology
Background:
- Gene regulatory networks (GRNs) are crucial for understanding gene expression.
- Current methods for GRN analysis are fragmented, requiring multiple tools and data sources, leading to high costs and time investment.
Purpose of the Study:
- To develop an integrated application for the inference, analysis, and visualization of gene regulatory modules and networks.
- To streamline the process of GRN analysis by consolidating data and tools.
Main Methods:
- Extended the BiologicalNetworks application with new tools for GRN inference and analysis.
- Integrated diverse biological data including gene expression, pathways, transcription factor binding sites, sequences, and annotations into a central database.
- Enabled analysis of multiple gene expression experiments and provided options for public or private data integration.
Main Results:
- Developed an application that integrates heterogeneous data for comprehensive GRN analysis.
- Facilitated the inference and analysis of gene regulatory modules and networks within a single environment.
- Enabled visualization of modular networks, regulatory modules, and binding sites.
Conclusions:
- The BiologicalNetworks application offers a unified platform for GRN research, reducing complexity and cost.
- The developed tools were successfully applied to biological case studies, including asthma models and embryonic stem cell networks.
- The application and its data are publicly accessible via http://www.biologicalnetworks.org.
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